Research & Education

chainerrl is a Python library designed for deep reinforcement learning, leveraging the Chainer framework. It offers a collection of advanced deep reinforcement learning algorithms, enabling researchers and developers to efficiently experiment with and apply these techniques. The library aims to facilitate the development and deployment of reinforcement learning solutions across various applications.

chemicalx is a specialized deep learning library designed for drug pair scoring. Built upon the robust PyTorch and TorchDrug frameworks, it offers capabilities for predicting drug interactions and analyzing chemical compounds. The library's primary goal is to support researchers in the fields of drug discovery and computational biology, providing them with essential tools for scoring and evaluating potential drug combinations. This facilitates more efficient and data-driven approaches to identifying effective therapeutic pairings.

Mars DTM Estimation is an AI-powered tool specifically designed for estimating digital terrain models (DTM) of Mars. This specialized application caters to the needs of researchers and scientists who are actively involved in analyzing and studying Martian terrain. The tool's primary purpose is to support scientific research endeavors and educational initiatives related to planetary science, providing valuable data for in-depth analysis and understanding of the Martian surface.

SWASTIKK AI TECH specializes in delivering advanced technology solutions across various domains. Their core offerings include robust cybersecurity measures, seamless AI implementation, and reliable cloud system support. Beyond these, they extend their expertise to IT, cybersecurity, and quantum technology consultancy services. The company is also actively involved in developing cutting-edge solutions for advanced space and defense technology, and crafting innovative blockchain applications. Their client base primarily consists of corporations and government agencies seeking sophisticated technological advancements and strategic guidance.

AMD Silo AI is an AI lab dedicated to creating and optimizing AI models specifically for AMD's compute platforms. As AMD’s global AI center of excellence, it employs a team of AI scientists and PhDs focused on advanced research. Their work extends to developing enterprise-ready AI solutions, assisting organizations in the integration and scaling of AI technologies. The lab aims to leverage AMD hardware capabilities to deliver high-performance AI.

Anari AI is at the forefront of AI hardware innovation, focusing on developing advanced AI chips. Their core offering is reconfigurable AI, which empowers customers to personalize their AI infrastructure to meet specific needs. The company's flagship product, the ThorX chip, is engineered to deliver superior computational efficiency compared to traditional GPUs, particularly when processing complex 3D and Graph data structures. Anari AI is dedicated to pushing the boundaries of AI hardware solutions, providing a novel and optimized approach to AI computing.

The XR5.0 Project is an AI research initiative dedicated to advancing Extended Reality (XR) technology. Its core focus is on creating and validating a person-centric, AI-driven XR paradigm specifically designed for Industry 5.0 applications. The project's primary goal is to demonstrate innovative XR solutions that effectively meet the stringent requirements of advanced industrial environments, emphasizing human-centric design within industrial processes.

Computer Science and Engineering at MBU is an academic program designed to advance tech careers through higher education. It provides comprehensive B.Tech and M.Tech degree programs. The curriculum is specifically structured to offer significant industry exposure, ensuring students gain practical and relevant skills. The primary goal is to equip students with the necessary knowledge and expertise in various facets of computer science and engineering, preparing them for successful careers in the technology sector.

Hybrid AI supports energy companies by optimizing their digitalization journey through user-centric software and services. The platform incorporates both data-driven and physics-based models to provide comprehensive solutions. Its primary goal is to empower energy companies to achieve more efficient, safer, and environmentally sound operations. Hybrid AI offers tailored solutions designed to address specific problems within the energy sector, focusing on practical applications for complex challenges.

LeddarTech specializes in automotive ADAS (Advanced Driver-Assistance Systems) and AD (Autonomous Driving) software. The company offers advanced raw data fusion and perception solutions designed to enhance vehicle safety and autonomy. Their flagship product, LeddarVision software, is capable of delivering highly accurate 3D environmental models. This software supports all SAE (Society of Automotive Engineers) autonomy levels, leveraging sophisticated AI and computer vision algorithms to process sensor data and create a comprehensive understanding of the vehicle's surroundings.

acados is a specialized software package designed for nonlinear optimal control and nonlinear model predictive control (NMPC). It focuses on delivering fast and embedded solvers, making it particularly suitable for real-time applications where computational efficiency is critical. The core of acados is written in C, ensuring high performance, and it offers convenient interfaces for popular programming languages such as Python and MATLAB, allowing a broader range of engineers and researchers to utilize its capabilities. Its design prioritizes deployment on embedded systems.

Mahotas is a Python library specifically designed for computer vision tasks. It stands out by providing a collection of high-performance computer vision algorithms, which are implemented in C++ to ensure optimal speed and efficiency. The library is built to seamlessly integrate with numpy arrays, making it a powerful tool for image processing and analysis within the Python ecosystem. Its focus on speed and integration with standard Python data structures makes it suitable for various computational imaging applications.

pykg2vec is a Python library specifically designed for knowledge graph embedding and representation learning. It provides a robust framework for researchers and practitioners to implement and experiment with a wide array of knowledge graph embedding algorithms. The library supports critical tasks within the knowledge graph domain, including link prediction and triplet classification, making it a valuable tool for advancing research and practical applications in this field.

pixel-nerf is a specialized neural radiance fields (NeRF) implementation focused on generating novel views from one or a few input images. This tool facilitates 3D scene reconstruction and image-based rendering, offering capabilities for creating detailed 3D representations from sparse 2D data. It is primarily designed for researchers and developers engaged in the fields of computer vision and graphics, providing a robust solution for advanced 3D modeling and visualization tasks.

ChosunTruck is an autonomous driving solution specifically developed for the popular game Euro Truck Simulator 2. This tool allows researchers to delve into the study and implementation of autonomous driving technology within a controlled and simulated environment. The core objective of the project is to accurately replicate real-world driving conditions and scenarios directly within the game. By doing so, ChosunTruck offers a valuable platform for testing, refining, and validating AI-driven vehicle control systems without the complexities and risks associated with real-world deployment.

contrastive-predictive-coding is a Keras-based tool that implements the Representation Learning with Contrastive Predictive Coding algorithm. Its primary function is to learn meaningful data representations by capturing semantic information without the need for explicit annotations. The tool leverages unsupervised learning methods to identify and recognize patterns within data, making it a valuable resource for advancing AI research and development. It is designed for those looking to explore and apply advanced representation learning techniques.

Vehicle-Detection-and-Tracking is a computer vision project designed for the detection and tracking of vehicles. It leverages the Tensorflow Object Detection API for robust detection capabilities and incorporates Kalman filtering for efficient tracking. The project offers a flexible framework, enabling developers to easily experiment with and compare various detection models and tracking algorithms. A core focus of the project is on maintaining code simplicity and readability, making it accessible for developers looking to implement or enhance vehicle detection and tracking systems.

Uzu is an AI inference engine engineered for high performance on Apple Silicon. It leverages a hybrid architecture that combines GPU kernels and MPSGraph to execute computations efficiently. The tool streamlines the integration of new AI models through unified model configurations, making it easier for developers to expand its capabilities. Additionally, Uzu provides traceable computations, ensuring the correctness and reliability of its AI model inferences.